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1# This file is part of obs_base.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <http://www.gnu.org/licenses/>.
22from __future__ import annotations
24__all__ = ("Instrument", "makeExposureRecordFromObsInfo", "addUnboundedCalibrationLabel", "loadCamera")
26import os.path
27from abc import ABCMeta, abstractmethod
28from typing import Any, Tuple, TYPE_CHECKING
29import astropy.time
31from lsst.afw.cameraGeom import Camera
32from lsst.daf.butler import (
33 Butler,
34 CollectionType,
35 DataCoordinate,
36 DataId,
37 DatasetType,
38 TIMESPAN_MIN,
39 TIMESPAN_MAX,
40)
41from lsst.utils import getPackageDir, doImport
43if TYPE_CHECKING: 43 ↛ 44line 43 didn't jump to line 44, because the condition on line 43 was never true
44 from .gen2to3 import TranslatorFactory
45 from lsst.daf.butler import Registry
47# To be a standard text curated calibration means that we use a
48# standard definition for the corresponding DatasetType.
49StandardCuratedCalibrationDatasetTypes = {
50 "defects": {"dimensions": ("instrument", "detector", "calibration_label"),
51 "storageClass": "Defects"},
52 "qe_curve": {"dimensions": ("instrument", "detector", "calibration_label"),
53 "storageClass": "QECurve"},
54 "crosstalk": {"dimensions": ("instrument", "detector", "calibration_label"),
55 "storageClass": "CrosstalkCalib"},
56}
59class Instrument(metaclass=ABCMeta):
60 """Base class for instrument-specific logic for the Gen3 Butler.
62 Concrete instrument subclasses should be directly constructable with no
63 arguments.
64 """
66 configPaths = ()
67 """Paths to config files to read for specific Tasks.
69 The paths in this list should contain files of the form `task.py`, for
70 each of the Tasks that requires special configuration.
71 """
73 policyName = None
74 """Instrument specific name to use when locating a policy or configuration
75 file in the file system."""
77 obsDataPackage = None
78 """Name of the package containing the text curated calibration files.
79 Usually a obs _data package. If `None` no curated calibration files
80 will be read. (`str`)"""
82 standardCuratedDatasetTypes = tuple(StandardCuratedCalibrationDatasetTypes)
83 """The dataset types expected to be obtained from the obsDataPackage.
84 These dataset types are all required to have standard definitions and
85 must be known to the base class. Clearing this list will prevent
86 any of these calibrations from being stored. If a dataset type is not
87 known to a specific instrument it can still be included in this list
88 since the data package is the source of truth.
89 """
91 @property
92 @abstractmethod
93 def filterDefinitions(self):
94 """`~lsst.obs.base.FilterDefinitionCollection`, defining the filters
95 for this instrument.
96 """
97 return None
99 def __init__(self):
100 self.filterDefinitions.reset()
101 self.filterDefinitions.defineFilters()
102 self._obsDataPackageDir = None
104 @classmethod
105 @abstractmethod
106 def getName(cls):
107 """Return the short (dimension) name for this instrument.
109 This is not (in general) the same as the class name - it's what is used
110 as the value of the "instrument" field in data IDs, and is usually an
111 abbreviation of the full name.
112 """
113 raise NotImplementedError()
115 @abstractmethod
116 def getCamera(self):
117 """Retrieve the cameraGeom representation of this instrument.
119 This is a temporary API that should go away once ``obs_`` packages have
120 a standardized approach to writing versioned cameras to a Gen3 repo.
121 """
122 raise NotImplementedError()
124 @abstractmethod
125 def register(self, registry):
126 """Insert instrument, physical_filter, and detector entries into a
127 `Registry`.
128 """
129 raise NotImplementedError()
131 @property
132 def obsDataPackageDir(self):
133 """The root of the obs package that provides specializations for
134 this instrument (`str`).
135 """
136 if self.obsDataPackage is None:
137 return None
138 if self._obsDataPackageDir is None:
139 # Defer any problems with locating the package until
140 # we need to find it.
141 self._obsDataPackageDir = getPackageDir(self.obsDataPackage)
142 return self._obsDataPackageDir
144 @staticmethod
145 def fromName(name: str, registry: Registry) -> Instrument:
146 """Given an instrument name and a butler, retrieve a corresponding
147 instantiated instrument object.
149 Parameters
150 ----------
151 name : `str`
152 Name of the instrument (must match the return value of `getName`).
153 registry : `lsst.daf.butler.Registry`
154 Butler registry to query to find the information.
156 Returns
157 -------
158 instrument : `Instrument`
159 An instance of the relevant `Instrument`.
161 Notes
162 -----
163 The instrument must be registered in the corresponding butler.
165 Raises
166 ------
167 LookupError
168 Raised if the instrument is not known to the supplied registry.
169 ModuleNotFoundError
170 Raised if the class could not be imported. This could mean
171 that the relevant obs package has not been setup.
172 TypeError
173 Raised if the class name retrieved is not a string.
174 """
175 dimensions = list(registry.queryDimensions("instrument", dataId={"instrument": name}))
176 cls = dimensions[0].records["instrument"].class_name
177 if not isinstance(cls, str):
178 raise TypeError(f"Unexpected class name retrieved from {name} instrument dimension (got {cls})")
179 instrument = doImport(cls)
180 return instrument()
182 @staticmethod
183 def importAll(registry: Registry) -> None:
184 """Import all the instruments known to this registry.
186 This will ensure that all metadata translators have been registered.
188 Parameters
189 ----------
190 registry : `lsst.daf.butler.Registry`
191 Butler registry to query to find the information.
193 Notes
194 -----
195 It is allowed for a particular instrument class to fail on import.
196 This might simply indicate that a particular obs package has
197 not been setup.
198 """
199 dimensions = list(registry.queryDimensions("instrument"))
200 for dim in dimensions:
201 cls = dim.records["instrument"].class_name
202 try:
203 doImport(cls)
204 except Exception:
205 pass
207 def _registerFilters(self, registry):
208 """Register the physical and abstract filter Dimension relationships.
209 This should be called in the ``register`` implementation.
211 Parameters
212 ----------
213 registry : `lsst.daf.butler.core.Registry`
214 The registry to add dimensions to.
215 """
216 for filter in self.filterDefinitions:
217 # fix for undefined abstract filters causing trouble in the registry:
218 if filter.abstract_filter is None:
219 abstract_filter = filter.physical_filter
220 else:
221 abstract_filter = filter.abstract_filter
223 registry.insertDimensionData("physical_filter",
224 {"instrument": self.getName(),
225 "name": filter.physical_filter,
226 "abstract_filter": abstract_filter
227 })
229 @abstractmethod
230 def getRawFormatter(self, dataId):
231 """Return the Formatter class that should be used to read a particular
232 raw file.
234 Parameters
235 ----------
236 dataId : `DataCoordinate`
237 Dimension-based ID for the raw file or files being ingested.
239 Returns
240 -------
241 formatter : `Formatter` class
242 Class to be used that reads the file into an
243 `lsst.afw.image.Exposure` instance.
244 """
245 raise NotImplementedError()
247 def writeCuratedCalibrations(self, butler, run=None):
248 """Write human-curated calibration Datasets to the given Butler with
249 the appropriate validity ranges.
251 Parameters
252 ----------
253 butler : `lsst.daf.butler.Butler`
254 Butler to use to store these calibrations.
255 run : `str`
256 Run to use for this collection of calibrations. If `None` the
257 collection name is worked out automatically from the instrument
258 name and other metadata.
260 Notes
261 -----
262 Expected to be called from subclasses. The base method calls
263 ``writeCameraGeom`` and ``writeStandardTextCuratedCalibrations``.
264 """
265 # Need to determine the run for ingestion based on the instrument
266 # name and eventually the data package version. The camera geom
267 # is currently special in that it is not in the _data package.
268 if run is None:
269 run = self.makeCollectionName("calib")
270 butler.registry.registerCollection(run, type=CollectionType.RUN)
271 self.writeCameraGeom(butler, run=run)
272 self.writeStandardTextCuratedCalibrations(butler, run=run)
273 self.writeAdditionalCuratedCalibrations(butler, run=run)
275 def writeAdditionalCuratedCalibrations(self, butler, run=None):
276 """Write additional curated calibrations that might be instrument
277 specific and are not part of the standard set.
279 Default implementation does nothing.
281 Parameters
282 ----------
283 butler : `lsst.daf.butler.Butler`
284 Butler to use to store these calibrations.
285 run : `str`, optional
286 Name of the run to use to override the default run associated
287 with this Butler.
288 """
289 return
291 def applyConfigOverrides(self, name, config):
292 """Apply instrument-specific overrides for a task config.
294 Parameters
295 ----------
296 name : `str`
297 Name of the object being configured; typically the _DefaultName
298 of a Task.
299 config : `lsst.pex.config.Config`
300 Config instance to which overrides should be applied.
301 """
302 for root in self.configPaths:
303 path = os.path.join(root, f"{name}.py")
304 if os.path.exists(path):
305 config.load(path)
307 def writeCameraGeom(self, butler, run=None):
308 """Write the default camera geometry to the butler repository
309 with an infinite validity range.
311 Parameters
312 ----------
313 butler : `lsst.daf.butler.Butler`
314 Butler to receive these calibration datasets.
315 run : `str`, optional
316 Name of the run to use to override the default run associated
317 with this Butler.
318 """
320 datasetType = DatasetType("camera", ("instrument", "calibration_label"), "Camera",
321 universe=butler.registry.dimensions)
322 butler.registry.registerDatasetType(datasetType)
323 unboundedDataId = addUnboundedCalibrationLabel(butler.registry, self.getName())
324 camera = self.getCamera()
325 butler.put(camera, datasetType, unboundedDataId, run=run)
327 def writeStandardTextCuratedCalibrations(self, butler, run=None):
328 """Write the set of standardized curated text calibrations to
329 the repository.
331 Parameters
332 ----------
333 butler : `lsst.daf.butler.Butler`
334 Butler to receive these calibration datasets.
335 run : `str`, optional
336 Name of the run to use to override the default run associated
337 with this Butler.
338 """
340 for datasetTypeName in self.standardCuratedDatasetTypes:
341 # We need to define the dataset types.
342 if datasetTypeName not in StandardCuratedCalibrationDatasetTypes:
343 raise ValueError(f"DatasetType {datasetTypeName} not in understood list"
344 f" [{'.'.join(StandardCuratedCalibrationDatasetTypes)}]")
345 definition = StandardCuratedCalibrationDatasetTypes[datasetTypeName]
346 datasetType = DatasetType(datasetTypeName,
347 universe=butler.registry.dimensions,
348 **definition)
349 self._writeSpecificCuratedCalibrationDatasets(butler, datasetType, run=run)
351 def _writeSpecificCuratedCalibrationDatasets(self, butler, datasetType, run=None):
352 """Write standardized curated calibration datasets for this specific
353 dataset type from an obs data package.
355 Parameters
356 ----------
357 butler : `lsst.daf.butler.Butler`
358 Gen3 butler in which to put the calibrations.
359 datasetType : `lsst.daf.butler.DatasetType`
360 Dataset type to be put.
361 run : `str`, optional
362 Name of the run to use to override the default run associated
363 with this Butler.
365 Notes
366 -----
367 This method scans the location defined in the ``obsDataPackageDir``
368 class attribute for curated calibrations corresponding to the
369 supplied dataset type. The directory name in the data package must
370 match the name of the dataset type. They are assumed to use the
371 standard layout and can be read by
372 `~lsst.pipe.tasks.read_curated_calibs.read_all` and provide standard
373 metadata.
374 """
375 if self.obsDataPackageDir is None:
376 # if there is no data package then there can't be datasets
377 return
379 calibPath = os.path.join(self.obsDataPackageDir, self.policyName,
380 datasetType.name)
382 if not os.path.exists(calibPath):
383 return
385 # Register the dataset type
386 butler.registry.registerDatasetType(datasetType)
388 # obs_base can't depend on pipe_tasks but concrete obs packages
389 # can -- we therefore have to defer import
390 from lsst.pipe.tasks.read_curated_calibs import read_all
392 camera = self.getCamera()
393 calibsDict = read_all(calibPath, camera)[0] # second return is calib type
394 endOfTime = TIMESPAN_MAX
395 dimensionRecords = []
396 datasetRecords = []
397 for det in calibsDict:
398 times = sorted([k for k in calibsDict[det]])
399 calibs = [calibsDict[det][time] for time in times]
400 times = [astropy.time.Time(t, format="datetime", scale="utc") for t in times]
401 times += [endOfTime]
402 for calib, beginTime, endTime in zip(calibs, times[:-1], times[1:]):
403 md = calib.getMetadata()
404 calibrationLabel = f"{datasetType.name}/{md['CALIBDATE']}/{md['DETECTOR']}"
405 dataId = DataCoordinate.standardize(
406 universe=butler.registry.dimensions,
407 instrument=self.getName(),
408 calibration_label=calibrationLabel,
409 detector=md["DETECTOR"],
410 )
411 datasetRecords.append((calib, dataId))
412 dimensionRecords.append({
413 "instrument": self.getName(),
414 "name": calibrationLabel,
415 "datetime_begin": beginTime,
416 "datetime_end": endTime,
417 })
419 # Second loop actually does the inserts and filesystem writes.
420 with butler.transaction():
421 butler.registry.insertDimensionData("calibration_label", *dimensionRecords)
422 # TODO: vectorize these puts, once butler APIs for that become
423 # available.
424 for calib, dataId in datasetRecords:
425 butler.put(calib, datasetType, dataId, run=run)
427 @abstractmethod
428 def makeDataIdTranslatorFactory(self) -> TranslatorFactory:
429 """Return a factory for creating Gen2->Gen3 data ID translators,
430 specialized for this instrument.
432 Derived class implementations should generally call
433 `TranslatorFactory.addGenericInstrumentRules` with appropriate
434 arguments, but are not required to (and may not be able to if their
435 Gen2 raw data IDs are sufficiently different from the HSC/DECam/CFHT
436 norm).
438 Returns
439 -------
440 factory : `TranslatorFactory`.
441 Factory for `Translator` objects.
442 """
443 raise NotImplementedError("Must be implemented by derived classes.")
445 @classmethod
446 def makeDefaultRawIngestRunName(cls) -> str:
447 """Make the default instrument-specific run collection string for raw
448 data ingest.
450 Returns
451 -------
452 coll : `str`
453 Run collection name to be used as the default for ingestion of
454 raws.
455 """
456 return cls.makeCollectionName("raw/all")
458 @classmethod
459 def makeCollectionName(cls, label: str) -> str:
460 """Get the instrument-specific collection string to use as derived
461 from the supplied label.
463 Parameters
464 ----------
465 label : `str`
466 String to be combined with the instrument name to form a
467 collection name.
469 Returns
470 -------
471 name : `str`
472 Collection name to use that includes the instrument name.
473 """
474 return f"{cls.getName()}/{label}"
477def makeExposureRecordFromObsInfo(obsInfo, universe):
478 """Construct an exposure DimensionRecord from
479 `astro_metadata_translator.ObservationInfo`.
481 Parameters
482 ----------
483 obsInfo : `astro_metadata_translator.ObservationInfo`
484 A `~astro_metadata_translator.ObservationInfo` object corresponding to
485 the exposure.
486 universe : `DimensionUniverse`
487 Set of all known dimensions.
489 Returns
490 -------
491 record : `DimensionRecord`
492 A record containing exposure metadata, suitable for insertion into
493 a `Registry`.
494 """
495 dimension = universe["exposure"]
496 return dimension.RecordClass.fromDict({
497 "instrument": obsInfo.instrument,
498 "id": obsInfo.exposure_id,
499 "name": obsInfo.observation_id,
500 "group_name": obsInfo.exposure_group,
501 "group_id": obsInfo.visit_id,
502 "datetime_begin": obsInfo.datetime_begin,
503 "datetime_end": obsInfo.datetime_end,
504 "exposure_time": obsInfo.exposure_time.to_value("s"),
505 "dark_time": obsInfo.dark_time.to_value("s"),
506 "observation_type": obsInfo.observation_type,
507 "physical_filter": obsInfo.physical_filter,
508 })
511def addUnboundedCalibrationLabel(registry, instrumentName):
512 """Add a special 'unbounded' calibration_label dimension entry for the
513 given camera that is valid for any exposure.
515 If such an entry already exists, this function just returns a `DataId`
516 for the existing entry.
518 Parameters
519 ----------
520 registry : `Registry`
521 Registry object in which to insert the dimension entry.
522 instrumentName : `str`
523 Name of the instrument this calibration label is associated with.
525 Returns
526 -------
527 dataId : `DataId`
528 New or existing data ID for the unbounded calibration.
529 """
530 d = dict(instrument=instrumentName, calibration_label="unbounded")
531 try:
532 return registry.expandDataId(d)
533 except LookupError:
534 pass
535 entry = d.copy()
536 entry["datetime_begin"] = TIMESPAN_MIN
537 entry["datetime_end"] = TIMESPAN_MAX
538 registry.insertDimensionData("calibration_label", entry)
539 return registry.expandDataId(d)
542def loadCamera(butler: Butler, dataId: DataId, *, collections: Any = None) -> Tuple[Camera, bool]:
543 """Attempt to load versioned camera geometry from a butler, but fall back
544 to obtaining a nominal camera from the `Instrument` class if that fails.
546 Parameters
547 ----------
548 butler : `lsst.daf.butler.Butler`
549 Butler instance to attempt to query for and load a ``camera`` dataset
550 from.
551 dataId : `dict` or `DataCoordinate`
552 Data ID that identifies at least the ``instrument`` and ``exposure``
553 dimensions.
554 collections : Any, optional
555 Collections to be searched, overriding ``self.butler.collections``.
556 Can be any of the types supported by the ``collections`` argument
557 to butler construction.
559 Returns
560 -------
561 camera : `lsst.afw.cameraGeom.Camera`
562 Camera object.
563 versioned : `bool`
564 If `True`, the camera was obtained from the butler and should represent
565 a versioned camera from a calibration repository. If `False`, no
566 camera datasets were found, and the returned camera was produced by
567 instantiating the appropriate `Instrument` class and calling
568 `Instrument.getCamera`.
569 """
570 if collections is None:
571 collections = butler.collections
572 # Registry would do data ID expansion internally if we didn't do it first,
573 # but we might want an expanded data ID ourselves later, so we do it here
574 # to ensure it only happens once.
575 # This will also catch problems with the data ID not having keys we need.
576 dataId = butler.registry.expandDataId(dataId, graph=butler.registry.dimensions["exposure"].graph)
577 cameraRefs = list(butler.registry.queryDatasets("camera", dataId=dataId, collections=collections,
578 deduplicate=True))
579 if cameraRefs:
580 assert len(cameraRefs) == 1, "Should be guaranteed by deduplicate=True above."
581 return butler.getDirect(cameraRefs[0]), True
582 instrument = Instrument.fromName(dataId["instrument"], butler.registry)
583 return instrument.getCamera(), False